Recently, as a personal identification means with no danger of loss (oblivion) and theft, a personal authentication technique using individual biometric information, such as a fingerprint, a face and the like has been increasingly studied. Biometrics information is roughly categorized into a physiological feature and a behavioral feature. A personal authentication technique is classified into a verification process (1:1 authentication) for determining whether a user is a specific person expressed by a card, a number or the like and an identification process (1:N authentication) for determining that a user is a specific person of registered persons.
A technique for generating a principal model necessary for individual authentication on the basis of the biometric feature information of an arbitrary person, of a plurality of pieces of biometric feature information and generating an invader model on the basis of the other pieces of biometric feature information, and a distance from a principal model and its distribution is known (for example, Patent document 1).
In biometric authentication, an image of a living body is picked up using a camera or the like and the features of the living body are extracted from the picked-up image. Then, it is verified whether a user is a principal, by collating the features with the pre-registered features of a single or a plurality of registration images.
Even in the same person, biometric information changes to some extent, depending on the difference of a collection environment, the change of a physical state. Therefore, in the individual authentication process it cannot be expected that registered data completely coincides with a verification data to be authenticated. Therefore, it is determined that a user is a principal, by the degree of similarity indicating how much a verification data to be authenticated is similar to the registered data.
When the degree of similarity is equal to or larger than a certain threshold (authentication threshold), it is determined that the user is an identical person. Otherwise, it is determined that the user is another person. It can also be determined whether a user is a principal, by comparing the degree of dissimilarity with the threshold, instead of the degree of similarity. Determination based on the degree of similarity or dissimilarity has a possibility that the following two types of errors may occur.
One is a case where a user is wrongly determined to be another person although the user is an identical person (identical person rejection).
The other is a case where a user is wrongly determined to be an identical person although the user is another person (Another person acceptance).
A rate at which the former error occurs is called a false rejection rate (FRR) and a rate at which the latter error occurs is called a false acceptance rate (FAR). Both are collectively called authentication accuracy. Authentication accuracy is one of the most important performance indexes.
When in determination based on the degree of dissimilarity, if an authentication threshold is reduced, FRR increases and FAR decreases. Conversely, if an authentication threshold is increased, FRR decreases and FAR increases. Thus, FRR and FAR are in a trade-off relation. Therefore, as the expression method of authentication accuracy, a FRR value in the case where FAR is below a certain value, a FAR value in the case where FRR is below a certain value or a set of several FRR and FAR values is used.
Authentication accuracy can be improved as follows.    1. Obtain learning data (training data) by measuring many subjects by a sensor, such a camera or the like.    2. Observe training data, and devise and realize a measurement method, a feature extraction method and a collation method that can be expected to be effective in reducing the number of errors of an individual authentication process.    3. Apply the verification method in the above step 2 to the training data in the above step 1 or a newly obtained training data (more particularly in the case where the modification of a measurement method is accompanied) and evaluate authentication accuracy.    4. Repeat the above steps 1 through 3 until targeted accuracy is obtained.
The above-described improvement method of authentication accuracy has the following problems. In order to improve the authentication accuracy of various types of data that exists in the world, many pieces of training data is necessary. Much cost is necessary for the collection and analysis of data. Since the measurement/extraction/collation methods of collected data are developed, excessive learning is easy to occur. Since the accuracy is often improved by fine adjustment for each piece of data, man-hours increase. Since a series of procedures for the improvement of authentication accuracy is executed by trial and error, it is difficult to make a stable development.
Patent document 1: Japanese Laid-open Patent Publication No. 2001-101406